ISSPA January 1 N -Gram and Local Context Analysis for Persian text retrieval Tehran University Abolfazl AleAhmad, Parsia Hakimian, Farzad Mahdikhani School of Electrical and Computer Engineering University of Tehran Farhad Oroumchian University of Wollongong in Dubai

2 University of Tehran - Database Research Group Outline The Persian Language Used Methods Pivoted normalization N-Gram approach Local Context Analysis The test collections Our experiment and the results Conclusion

3 University of Tehran - Database Research Group Outline  The Persian Language Used Methods Pivoted normalization N-Gram approach Local Context Analysis Our test collections Our experiment and the results Conclusion

4 University of Tehran - Database Research Group The Persian Language It is Spoken in countries like Iran, Tajikistan and Afghanistan It has Arabic like script for writing and consists of 32 characters that are written continuously from right to left It’s morphological analyzers need to deal with many forms of words that are not actually Farsi Example The word “کافر” (singular)  “کفار” (plural) Or “عادت” that has two plural forms in Farsi: –Farsi form“عادت ها” –Arabic form“عادات” So N-Grams are a solution

5 University of Tehran - Database Research Group Our Study We investigated vector space model on the Persian language: unstemmed single term N-gram based Local Context Analysis Using HAMSHAHRI collection which contains 160,000+ news articles

6 University of Tehran - Database Research Group Outline  The Persian Language Used Methods Pivoted normalization N-Gram approach Local Context Analysis Our test collections Our experiment and the results Conclusion

7 University of Tehran - Database Research Group NameWeighting tf.idf tf*log(N/n) / (  (tf 2 ) *  (qtf 2 )) lnc.ltc (1+log(tf))*(1+log(qtf))*log((1+N)/n) / (  (tf 2 ) *  (qtf 2 )) nxx.bpx ( *tf/max tf)+log((N-n)/n) tfc.nfc tf*log(N/n)*( *qtf/max qtf)*log(N/n) / (  (tf 2 ) *  (qtf 2 )) tfc.nfx1 tf* log(N/n)*( *qtf/max qtf) *log(N/n) / (  (tf * log(N/n)) 2 ) tfc.nfx2 tf*log(N/n)*( *qtf/max qtf)*log(N/n) / (  (tf 2 )) Lnu.ltu ((1+log(tf))*(1+log(qtf))*log((1+N)/n))/ ((1+log(average tf)) * ((1-s) + s * N.U.W/ average N.U.W) 2) List of Weights that produced the best results Best Vector Space Model

8 University of Tehran - Database Research Group Problem with Document length normalization It is supposed to remove the difference between the document's lengths Under cosine normalization shorter documents get higher weights but they are less relevant. Average of median bin length Average probability of Relevance/Retrieval

9 University of Tehran - Database Research Group Lnu.ltu weighting scheme A good weight proposed by Amit Singhal, et al. and tested on TREC collections Based on reducing the gap between relevance and retrieval Lnu = ltu =

10 University of Tehran - Database Research Group Pivoted Normalization Document Length Probability Final Normalization Factor Old Normalization Factor Source: A. Singhal, et al. “Pivoted Document Length Normalization”

11 University of Tehran - Database Research Group Outline  The Persian Language Used Methods Pivoted normalization N-Gram approach Local Context Analysis Our test collections Our experiment and the results Conclusion

12 University of Tehran - Database Research Group NGRAMS are strings of length n. In this approach the whole text is considered as a stream of characters and then it is broken down to substrings of length n. It is remarkably resistant to textual errors (e.g. OCR) and no linguistic knowledge is needed. Example: “مخابرات” for n=4 مخاب خابر ابرا برات رات NGRAM Approach (Cont.)

13 University of Tehran - Database Research Group Outline  The Persian Language Used Methods Pivoted normalization N-Gram approach Local Context Analysis Our test collections Our experiment and the results Conclusion

14 University of Tehran - Database Research Group Word Mismatch Problem Automatic query expansion is a good solution for the issue of word mismatch in IR: Local Analysis + Expansion based on high ranking documents - Needs an extra search - Some queries may retrieve few relevant documents Global Analysis + It has robust average performance - Expensive in terms of disk space and CPU - Individual Queries can be significantly degraded

15 University of Tehran - Database Research Group Local Context Analysis Local Context Analysis is an automatic query expansion method combines global analysis (use of context & phrase structure) and local feedback (Top ranked documents) LCA is fully automated and there is no need to collect any information from user other than the initial query + It is computationally practical - But has the extra search to retrieve top ranked documents

16 University of Tehran - Database Research Group LCA has three main steps: 1. Run user’s query, break the top N retrieved documents into passages and rank them again. 2. Calculate similarity of each concept in the top ranked passages with the entire original query using similarity function: 3. the top M ranked concepts are added to the original query and initial retrieval method is done with the expanded query Local Context Analysis (Cont.)

17 University of Tehran - Database Research Group Outline  The Persian Language Used Methods Pivoted normalization N-Gram approach Local Context Analysis Our test collections Our experiment and the results Conclusion

18 University of Tehran - Database Research Group Test Collections  Qvanin Collection Documents: Iranian Law Collection passages 41 queries and Relevance Judgments  Hamshari Collection Documents: 600+ MB News from Hamshari Newspaper news articles 60 queries and Relevance Judgments  BijanKhan Tagged Collection Documents: 100+ MB from different sources A tag set of 41 tags tagged words

19 University of Tehran - Database Research Group Hamshahri Collection We used HAMSHAHRI (a test collection for Persian text prepared and distributed by DBRG (IR team) of University of Tehran) The 3 rd version: –contains about distinct textual news articles in Farsi –60 queries and relevance judgments for top 20 relevant documents for each query

20 University of Tehran - Database Research Group Some examples of Queries Women rights law قانون حقوق زنان Contamination in Persian gulf آلودگی خلیج فارس Birds migration کوچ پرندگان Increase of gas price افزایش قیمت بنزین Iranian Wrestling کشتی فرنگی ایران

21 University of Tehran - Database Research Group Outline The Persian Language Used Methods Pivoted normalization N-Gram approach Local Context Analysis Our test collections Our experiment and the results Conclusion 

22 University of Tehran - Database Research Group Term-based vector space model A. Singhal, et al. in their paper “Pivoted Document Length Normalization” reported that the following two configurations have the best performance: Slope=0.25 and using pivoted unique normalization (P.U.N.). Pivot = average no. of unique terms in a document Slope=0.75 and using pivoted cosine normalization (P.C.N.). Pivot = average cosine factor for 1+log(tf)

23 University of Tehran - Database Research Group Our experiment results Comparison of vector space model slope=0.25 and slope=0.75

24 University of Tehran - Database Research Group Our experiment results Comparison of vector space model and LCA : In LCA we used Lnu.ltu (slope=0.25 and P.U.N.)

25 University of Tehran - Database Research Group N-Gram Experiments Next, we assessed N-gram based vector space model for N = 3,4,5 on the HAMSHAHRI collection. In addition to Lnu.ltu we assessed atc.atc in which both query and documents are weighted as follows: atc =

26 University of Tehran - Database Research Group N-Gram experiment results N-Grams using atc.atc and lnu.ltu (slope=0.25) weighting schemes

27 University of Tehran - Database Research Group Previous Works: Comparison of Vector Space System with FuFaIR They used the first version of HAMSHAHRI collection (300+ MB) in Their experiments. It has 30 Queries In vector space model the Slope set to 0.75 and the Pivot set to Conclusion

28 University of Tehran - Database Research Group Comparison of vector space systems with BM25 Conclusion

29 University of Tehran - Database Research Group Experiments on Qavanin Collection Conclusion Source: F. Oroumchian, F. Mazhar Garamaleki. “An Evaluation of Retrieval performance Using Farsi Text”. First Eurasia Conference on Advances in Information and Communication Technology, Tehran, Iran, October Comparison of Best Vector Space With Best N-grams

30 University of Tehran - Database Research Group Our experiment best results Experiments using atc.atc and lnu.ltu (slope=0.25) weighting schemes

31 University of Tehran - Database Research Group Results Analysis (N-Gram) AS It was shown, 4-gram based vector space with Lnu.ltu weighting scheme has better performance than FuFaIR and other vector space models: It is in contradiction with the performance of them in English. The rational is that most Farsi words' roots are about 4 characters. Our results are more valid than previous works because we used a better collection Conclusion

32 University of Tehran - Database Research Group Results Analysis (LCA) Local Context Analysis only marginally improved the results over the Lnu.ltu method Lnu.ltu weighting method is performing very well on the Farsi language It’s better to tune LCA parameters for the HAMSHAHRI collection Conclusion

33 University of Tehran - Database Research Group Thanks, Questions ?